Overview

Brought to you by YData

Dataset statistics

Number of variables49
Number of observations203002
Missing cells555
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.0 MiB
Average record size in memory191.2 B

Variable types

Numeric13
DateTime5
Categorical28
Boolean3

Alerts

id_empresa has constant value "1" Constant
nombre_empresa has constant value "Hotel 1" Constant
total_habitaciones_empresa has constant value "735" Constant
cupo_tipo_habitacion has constant value "2.0" Constant
id_moneda has constant value "1" Constant
id_agencia has a high cardinality: 120 distinct values High cardinality
nombre_agencia has a high cardinality: 112 distinct values High cardinality
ciudad_agencia has a high cardinality: 56 distinct values High cardinality
clave_estado has a high cardinality: 148 distinct values High cardinality
nombre_estado has a high cardinality: 144 distinct values High cardinality
ciudad_agencia is highly overall correlated with entidad_federativa_agencia and 3 other fieldsHigh correlation
clasificacion_tipo_habitacion is highly overall correlated with id_tipo_habitacion and 1 other fieldsHigh correlation
cliente_disp_anio_anterior is highly overall correlated with id_reservaciones and 12 other fieldsHigh correlation
entidad_federativa_agencia is highly overall correlated with ciudad_agencia and 1 other fieldsHigh correlation
id_canal is highly overall correlated with nombre_canalHigh correlation
id_cliente_disp is highly overall correlated with numero_adultos and 4 other fieldsHigh correlation
id_estatus_reservacion is highly overall correlated with nombre_estatus_reservacionHigh correlation
id_pais_origen is highly overall correlated with nombre_pais_origenHigh correlation
id_paquete is highly overall correlated with id_programa and 2 other fieldsHigh correlation
id_programa is highly overall correlated with id_paquete and 2 other fieldsHigh correlation
id_reservaciones is highly overall correlated with cliente_disp_anio_anterior and 12 other fieldsHigh correlation
id_segmento is highly overall correlated with ciudad_agencia and 2 other fieldsHigh correlation
id_tipo_habitacion is highly overall correlated with clasificacion_tipo_habitacion and 1 other fieldsHigh correlation
nombre_canal is highly overall correlated with id_canalHigh correlation
nombre_estatus_reservacion is highly overall correlated with id_estatus_reservacionHigh correlation
nombre_pais_origen is highly overall correlated with id_pais_origenHigh correlation
nombre_paquete is highly overall correlated with id_paquete and 2 other fieldsHigh correlation
nombre_programa is highly overall correlated with id_paquete and 2 other fieldsHigh correlation
nombre_segmento is highly overall correlated with ciudad_agencia and 2 other fieldsHigh correlation
nombre_tipo_habitacion is highly overall correlated with clasificacion_tipo_habitacion and 1 other fieldsHigh correlation
numero_adultos is highly overall correlated with cliente_disp_anio_anterior and 13 other fieldsHigh correlation
numero_adultos_anio_anterior is highly overall correlated with cliente_disp_anio_anterior and 12 other fieldsHigh correlation
numero_noches is highly overall correlated with cliente_disp_anio_anterior and 9 other fieldsHigh correlation
numero_noches_anio_anterior is highly overall correlated with cliente_disp_anio_anterior and 9 other fieldsHigh correlation
numero_personas is highly overall correlated with cliente_disp_anio_anterior and 10 other fieldsHigh correlation
numero_personas_anio_anterior is highly overall correlated with cliente_disp_anio_anterior and 9 other fieldsHigh correlation
pais_agencia is highly overall correlated with ciudad_agencia and 3 other fieldsHigh correlation
reservacion is highly overall correlated with cliente_disp_anio_anterior and 8 other fieldsHigh correlation
reservacion_anio_anterior is highly overall correlated with cliente_disp_anio_anterior and 8 other fieldsHigh correlation
reservacion_pendiente is highly overall correlated with cliente_disp_anio_anterior and 8 other fieldsHigh correlation
total_habitaciones is highly overall correlated with cliente_disp_anio_anterior and 12 other fieldsHigh correlation
total_habitaciones_anio_anterior is highly overall correlated with cliente_disp_anio_anterior and 12 other fieldsHigh correlation
total_tarifa is highly overall correlated with cliente_disp_anio_anterior and 9 other fieldsHigh correlation
id_programa is highly imbalanced (97.5%) Imbalance
nombre_programa is highly imbalanced (97.5%) Imbalance
id_paquete is highly imbalanced (60.9%) Imbalance
nombre_paquete is highly imbalanced (60.9%) Imbalance
pais_agencia is highly imbalanced (86.1%) Imbalance
id_pais_origen is highly imbalanced (96.3%) Imbalance
nombre_pais_origen is highly imbalanced (96.3%) Imbalance
clave_estado is highly imbalanced (55.8%) Imbalance
nombre_estado is highly imbalanced (55.5%) Imbalance
id_cliente_disp is highly imbalanced (60.9%) Imbalance
numero_noches is highly skewed (γ1 = 81.01891754) Skewed
numero_noches_anio_anterior is highly skewed (γ1 = 82.30675646) Skewed
total_tarifa is highly skewed (γ1 = 26.89402197) Skewed
id_reservaciones is uniformly distributed Uniform
id_reservaciones has unique values Unique
numero_personas has 100803 (49.7%) zeros Zeros
numero_personas_anio_anterior has 102199 (50.3%) zeros Zeros
numero_adultos has 100803 (49.7%) zeros Zeros
numero_adultos_anio_anterior has 102199 (50.3%) zeros Zeros
numero_menores has 198413 (97.7%) zeros Zeros
numero_menores_anio_anterior has 198466 (97.8%) zeros Zeros
numero_noches has 101006 (49.8%) zeros Zeros
numero_noches_anio_anterior has 102402 (50.4%) zeros Zeros
total_habitaciones has 101049 (49.8%) zeros Zeros
total_habitaciones_anio_anterior has 102445 (50.5%) zeros Zeros
total_tarifa has 106391 (52.4%) zeros Zeros
cliente_disp_anio_anterior has 102199 (50.3%) zeros Zeros

Reproduction

Analysis started2025-05-11 19:29:04.390027
Analysis finished2025-05-11 19:29:57.886285
Duration53.5 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

id_reservaciones
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct203002
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean101500.5
Minimum0
Maximum203001
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:29:58.006355image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10150.05
Q150750.25
median101500.5
Q3152250.75
95-th percentile192850.95
Maximum203001
Range203001
Interquartile range (IQR)101500.5

Descriptive statistics

Standard deviation58601.774
Coefficient of variation (CV)0.57735454
Kurtosis-1.2
Mean101500.5
Median Absolute Deviation (MAD)50750.5
Skewness-8.5222189 × 10-16
Sum2.0604805 × 1010
Variance3.4341679 × 109
MonotonicityStrictly increasing
2025-05-11T19:29:58.164241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
203001 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
Other values (202992) 202992
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
203001 1
< 0.1%
203000 1
< 0.1%
202999 1
< 0.1%
202998 1
< 0.1%
202997 1
< 0.1%
202996 1
< 0.1%
202995 1
< 0.1%
202994 1
< 0.1%
202993 1
< 0.1%
202992 1
< 0.1%
Distinct824
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2019-01-02 00:00:00
Maximum2021-12-03 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-11T19:29:58.326544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:58.500794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct482
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
Minimum2019-01-02 00:00:00
Maximum2020-12-04 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-11T19:29:58.637778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:58.790443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct713
Distinct (%)0.4%
Missing2
Missing (%)< 0.1%
Memory size1.5 MiB
Minimum2019-01-03 00:00:00
Maximum2021-12-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-11T19:29:58.940261image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:59.086364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct722
Distinct (%)0.4%
Missing34
Missing (%)< 0.1%
Memory size1.5 MiB
Minimum2019-01-03 00:00:00
Maximum2021-12-06 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-11T19:29:59.229522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:59.374494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

numero_personas
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1858652
Minimum0
Maximum32
Zeros100803
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:29:59.501144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3696288
Coefficient of variation (CV)1.1549616
Kurtosis17.748518
Mean1.1858652
Median Absolute Deviation (MAD)1
Skewness1.7961185
Sum240733
Variance1.875883
MonotonicityNot monotonic
2025-05-11T19:29:59.601396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 100803
49.7%
2 71889
35.4%
3 15053
 
7.4%
4 7789
 
3.8%
1 4928
 
2.4%
5 1280
 
0.6%
6 785
 
0.4%
8 228
 
0.1%
7 96
 
< 0.1%
12 40
 
< 0.1%
Other values (12) 111
 
0.1%
ValueCountFrequency (%)
0 100803
49.7%
1 4928
 
2.4%
2 71889
35.4%
3 15053
 
7.4%
4 7789
 
3.8%
5 1280
 
0.6%
6 785
 
0.4%
7 96
 
< 0.1%
8 228
 
0.1%
9 17
 
< 0.1%
ValueCountFrequency (%)
32 10
 
< 0.1%
20 2
 
< 0.1%
19 5
 
< 0.1%
18 6
 
< 0.1%
17 13
 
< 0.1%
16 8
 
< 0.1%
15 2
 
< 0.1%
14 6
 
< 0.1%
13 3
 
< 0.1%
12 40
< 0.1%

numero_personas_anio_anterior
Real number (ℝ)

High correlation  Zeros 

Distinct22
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.17055
Minimum0
Maximum32
Zeros102199
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:29:59.717460image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile4
Maximum32
Range32
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3680725
Coefficient of variation (CV)1.1687433
Kurtosis17.777041
Mean1.17055
Median Absolute Deviation (MAD)0
Skewness1.8060301
Sum237624
Variance1.8716224
MonotonicityNot monotonic
2025-05-11T19:29:59.816952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 102199
50.3%
2 70655
34.8%
3 14990
 
7.4%
4 7732
 
3.8%
1 4913
 
2.4%
5 1276
 
0.6%
6 779
 
0.4%
8 215
 
0.1%
7 96
 
< 0.1%
12 38
 
< 0.1%
Other values (12) 109
 
0.1%
ValueCountFrequency (%)
0 102199
50.3%
1 4913
 
2.4%
2 70655
34.8%
3 14990
 
7.4%
4 7732
 
3.8%
5 1276
 
0.6%
6 779
 
0.4%
7 96
 
< 0.1%
8 215
 
0.1%
9 17
 
< 0.1%
ValueCountFrequency (%)
32 10
 
< 0.1%
20 2
 
< 0.1%
19 5
 
< 0.1%
18 6
 
< 0.1%
17 13
 
< 0.1%
16 8
 
< 0.1%
15 1
 
< 0.1%
14 6
 
< 0.1%
13 3
 
< 0.1%
12 38
< 0.1%

numero_adultos
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0947084
Minimum0
Maximum15
Zeros100803
Zeros (%)49.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:29:59.912171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2070762
Coefficient of variation (CV)1.1026463
Kurtosis4.3490022
Mean1.0947084
Median Absolute Deviation (MAD)1
Skewness1.057326
Sum222228
Variance1.4570329
MonotonicityNot monotonic
2025-05-11T19:30:00.017157image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 100803
49.7%
2 80295
39.6%
3 12016
 
5.9%
1 5654
 
2.8%
4 2962
 
1.5%
5 536
 
0.3%
6 427
 
0.2%
8 156
 
0.1%
7 43
 
< 0.1%
15 24
 
< 0.1%
Other values (6) 86
 
< 0.1%
ValueCountFrequency (%)
0 100803
49.7%
1 5654
 
2.8%
2 80295
39.6%
3 12016
 
5.9%
4 2962
 
1.5%
5 536
 
0.3%
6 427
 
0.2%
7 43
 
< 0.1%
8 156
 
0.1%
9 20
 
< 0.1%
ValueCountFrequency (%)
15 24
 
< 0.1%
14 8
 
< 0.1%
13 4
 
< 0.1%
12 22
 
< 0.1%
11 9
 
< 0.1%
10 23
 
< 0.1%
9 20
 
< 0.1%
8 156
 
0.1%
7 43
 
< 0.1%
6 427
0.2%

numero_adultos_anio_anterior
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0796938
Minimum0
Maximum15
Zeros102199
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:30:00.124998image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2046937
Coefficient of variation (CV)1.1157735
Kurtosis4.2151962
Mean1.0796938
Median Absolute Deviation (MAD)0
Skewness1.0595136
Sum219180
Variance1.451287
MonotonicityNot monotonic
2025-05-11T19:30:00.233893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 102199
50.3%
2 79038
38.9%
3 11946
 
5.9%
1 5638
 
2.8%
4 2933
 
1.4%
5 534
 
0.3%
6 422
 
0.2%
8 143
 
0.1%
7 43
 
< 0.1%
10 23
 
< 0.1%
Other values (6) 83
 
< 0.1%
ValueCountFrequency (%)
0 102199
50.3%
1 5638
 
2.8%
2 79038
38.9%
3 11946
 
5.9%
4 2933
 
1.4%
5 534
 
0.3%
6 422
 
0.2%
7 43
 
< 0.1%
8 143
 
0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
15 23
 
< 0.1%
14 8
 
< 0.1%
13 4
 
< 0.1%
12 20
 
< 0.1%
11 9
 
< 0.1%
10 23
 
< 0.1%
9 19
 
< 0.1%
8 143
 
0.1%
7 43
 
< 0.1%
6 422
0.2%

numero_menores
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.034108038
Minimum0
Maximum15
Zeros198413
Zeros (%)97.7%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:30:00.335567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26518667
Coefficient of variation (CV)7.7749023
Kurtosis582.05071
Mean0.034108038
Median Absolute Deviation (MAD)0
Skewness16.064875
Sum6924
Variance0.070323969
MonotonicityNot monotonic
2025-05-11T19:30:00.425797image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 198413
97.7%
1 2730
 
1.3%
2 1619
 
0.8%
3 168
 
0.1%
4 31
 
< 0.1%
5 17
 
< 0.1%
15 10
 
< 0.1%
7 7
 
< 0.1%
6 6
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 198413
97.7%
1 2730
 
1.3%
2 1619
 
0.8%
3 168
 
0.1%
4 31
 
< 0.1%
5 17
 
< 0.1%
6 6
 
< 0.1%
7 7
 
< 0.1%
8 1
 
< 0.1%
15 10
 
< 0.1%
ValueCountFrequency (%)
15 10
 
< 0.1%
8 1
 
< 0.1%
7 7
 
< 0.1%
6 6
 
< 0.1%
5 17
 
< 0.1%
4 31
 
< 0.1%
3 168
 
0.1%
2 1619
 
0.8%
1 2730
 
1.3%
0 198413
97.7%

numero_menores_anio_anterior
Real number (ℝ)

Zeros 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.033733658
Minimum0
Maximum15
Zeros198466
Zeros (%)97.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:30:00.535080image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.26404323
Coefficient of variation (CV)7.8272933
Kurtosis591.82119
Mean0.033733658
Median Absolute Deviation (MAD)0
Skewness16.215274
Sum6848
Variance0.069718829
MonotonicityNot monotonic
2025-05-11T19:30:00.624501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 198466
97.8%
1 2697
 
1.3%
2 1602
 
0.8%
3 165
 
0.1%
4 31
 
< 0.1%
5 17
 
< 0.1%
15 10
 
< 0.1%
7 7
 
< 0.1%
6 6
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 198466
97.8%
1 2697
 
1.3%
2 1602
 
0.8%
3 165
 
0.1%
4 31
 
< 0.1%
5 17
 
< 0.1%
6 6
 
< 0.1%
7 7
 
< 0.1%
8 1
 
< 0.1%
15 10
 
< 0.1%
ValueCountFrequency (%)
15 10
 
< 0.1%
8 1
 
< 0.1%
7 7
 
< 0.1%
6 6
 
< 0.1%
5 17
 
< 0.1%
4 31
 
< 0.1%
3 165
 
0.1%
2 1602
 
0.8%
1 2697
 
1.3%
0 198466
97.8%

numero_noches
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct42
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6800278
Minimum0
Maximum687
Zeros101006
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:30:00.741978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum687
Range687
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.866189
Coefficient of variation (CV)1.7060367
Kurtosis17214.397
Mean1.6800278
Median Absolute Deviation (MAD)1
Skewness81.018918
Sum341049
Variance8.2150394
MonotonicityNot monotonic
2025-05-11T19:30:00.874404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 101006
49.8%
3 38707
 
19.1%
2 28782
 
14.2%
4 16407
 
8.1%
7 8683
 
4.3%
1 4702
 
2.3%
5 2511
 
1.2%
6 1034
 
0.5%
14 643
 
0.3%
8 142
 
0.1%
Other values (32) 385
 
0.2%
ValueCountFrequency (%)
0 101006
49.8%
1 4702
 
2.3%
2 28782
 
14.2%
3 38707
 
19.1%
4 16407
 
8.1%
5 2511
 
1.2%
6 1034
 
0.5%
7 8683
 
4.3%
8 142
 
0.1%
9 67
 
< 0.1%
ValueCountFrequency (%)
687 1
 
< 0.1%
321 1
 
< 0.1%
191 1
 
< 0.1%
182 3
< 0.1%
89 2
< 0.1%
87 2
< 0.1%
84 1
 
< 0.1%
81 1
 
< 0.1%
74 1
 
< 0.1%
63 2
< 0.1%

numero_noches_anio_anterior
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct41
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6560428
Minimum0
Maximum687
Zeros102402
Zeros (%)50.4%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:30:01.979453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile6
Maximum687
Range687
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8479579
Coefficient of variation (CV)1.7197369
Kurtosis17653.507
Mean1.6560428
Median Absolute Deviation (MAD)0
Skewness82.306756
Sum336180
Variance8.1108641
MonotonicityNot monotonic
2025-05-11T19:30:02.189002image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0 102402
50.4%
3 38025
 
18.7%
2 28423
 
14.0%
4 16169
 
8.0%
7 8623
 
4.2%
1 4696
 
2.3%
5 2496
 
1.2%
6 1032
 
0.5%
14 643
 
0.3%
8 140
 
0.1%
Other values (31) 353
 
0.2%
ValueCountFrequency (%)
0 102402
50.4%
1 4696
 
2.3%
2 28423
 
14.0%
3 38025
 
18.7%
4 16169
 
8.0%
5 2496
 
1.2%
6 1032
 
0.5%
7 8623
 
4.2%
8 140
 
0.1%
9 67
 
< 0.1%
ValueCountFrequency (%)
687 1
 
< 0.1%
321 1
 
< 0.1%
191 1
 
< 0.1%
182 3
< 0.1%
87 2
< 0.1%
84 1
 
< 0.1%
81 1
 
< 0.1%
74 1
 
< 0.1%
63 2
< 0.1%
59 1
 
< 0.1%

total_habitaciones
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.50558123
Minimum0
Maximum6
Zeros101049
Zeros (%)49.8%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:30:02.342077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.50875061
Coefficient of variation (CV)1.0062688
Kurtosis-1.2383857
Mean0.50558123
Median Absolute Deviation (MAD)1
Skewness0.1342456
Sum102634
Variance0.25882718
MonotonicityNot monotonic
2025-05-11T19:30:02.467421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 101467
50.0%
0 101049
49.8%
2 308
 
0.2%
3 166
 
0.1%
4 8
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 101049
49.8%
1 101467
50.0%
2 308
 
0.2%
3 166
 
0.1%
4 8
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 3
 
< 0.1%
4 8
 
< 0.1%
3 166
 
0.1%
2 308
 
0.2%
1 101467
50.0%
0 101049
49.8%

total_habitaciones_anio_anterior
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.49858622
Minimum0
Maximum6
Zeros102445
Zeros (%)50.5%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:30:02.620383image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.50848871
Coefficient of variation (CV)1.0198611
Kurtosis-1.2480827
Mean0.49858622
Median Absolute Deviation (MAD)0
Skewness0.15773602
Sum101214
Variance0.25856077
MonotonicityNot monotonic
2025-05-11T19:30:02.745299image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 102445
50.5%
1 100089
49.3%
2 296
 
0.1%
3 160
 
0.1%
4 8
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 102445
50.5%
1 100089
49.3%
2 296
 
0.1%
3 160
 
0.1%
4 8
 
< 0.1%
5 3
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 3
 
< 0.1%
4 8
 
< 0.1%
3 160
 
0.1%
2 296
 
0.1%
1 100089
49.3%
0 102445
50.5%

id_programa
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.5 KiB
1
202510 
0
 
492

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters203002
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 202510
99.8%
0 492
 
0.2%

Length

2025-05-11T19:30:02.900481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:02.991756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 202510
99.8%
0 492
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 202510
99.8%
0 492
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 202510
99.8%
0 492
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 202510
99.8%
0 492
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 202510
99.8%
0 492
 
0.2%

nombre_programa
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.5 KiB
Ninguno
202510 
Sin Definir
 
492

Length

Max length11
Median length7
Mean length7.0096945
Min length7

Characters and Unicode

Total characters1422982
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNinguno
2nd rowNinguno
3rd rowNinguno
4th rowNinguno
5th rowNinguno

Common Values

ValueCountFrequency (%)
Ninguno 202510
99.8%
Sin Definir 492
 
0.2%

Length

2025-05-11T19:30:03.130301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:03.247961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
ninguno 202510
99.5%
sin 492
 
0.2%
definir 492
 
0.2%

Most occurring characters

ValueCountFrequency (%)
n 406004
28.5%
i 203986
14.3%
N 202510
14.2%
g 202510
14.2%
u 202510
14.2%
o 202510
14.2%
S 492
 
< 0.1%
492
 
< 0.1%
D 492
 
< 0.1%
e 492
 
< 0.1%
Other values (2) 984
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1422982
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 406004
28.5%
i 203986
14.3%
N 202510
14.2%
g 202510
14.2%
u 202510
14.2%
o 202510
14.2%
S 492
 
< 0.1%
492
 
< 0.1%
D 492
 
< 0.1%
e 492
 
< 0.1%
Other values (2) 984
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1422982
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 406004
28.5%
i 203986
14.3%
N 202510
14.2%
g 202510
14.2%
u 202510
14.2%
o 202510
14.2%
S 492
 
< 0.1%
492
 
< 0.1%
D 492
 
< 0.1%
e 492
 
< 0.1%
Other values (2) 984
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1422982
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 406004
28.5%
i 203986
14.3%
N 202510
14.2%
g 202510
14.2%
u 202510
14.2%
o 202510
14.2%
S 492
 
< 0.1%
492
 
< 0.1%
D 492
 
< 0.1%
e 492
 
< 0.1%
Other values (2) 984
 
0.1%

id_paquete
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.6 KiB
1
117658 
2
84718 
0
 
492
5
 
92
3
 
40

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters203002
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row1
4th row2
5th row1

Common Values

ValueCountFrequency (%)
1 117658
58.0%
2 84718
41.7%
0 492
 
0.2%
5 92
 
< 0.1%
3 40
 
< 0.1%
4 2
 
< 0.1%

Length

2025-05-11T19:30:03.371662image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:03.494767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 117658
58.0%
2 84718
41.7%
0 492
 
0.2%
5 92
 
< 0.1%
3 40
 
< 0.1%
4 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
1 117658
58.0%
2 84718
41.7%
0 492
 
0.2%
5 92
 
< 0.1%
3 40
 
< 0.1%
4 2
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 117658
58.0%
2 84718
41.7%
0 492
 
0.2%
5 92
 
< 0.1%
3 40
 
< 0.1%
4 2
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 117658
58.0%
2 84718
41.7%
0 492
 
0.2%
5 92
 
< 0.1%
3 40
 
< 0.1%
4 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 117658
58.0%
2 84718
41.7%
0 492
 
0.2%
5 92
 
< 0.1%
3 40
 
< 0.1%
4 2
 
< 0.1%

nombre_paquete
Categorical

High correlation  Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.6 KiB
Walk In
117658 
Ninguno
84718 
Sin Definir
 
492
Entre Semana
 
92
Lunamielero
 
40

Length

Max length13
Median length7
Mean length7.0128078
Min length7

Characters and Unicode

Total characters1423614
Distinct characters22
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWalk In
2nd rowNinguno
3rd rowWalk In
4th rowNinguno
5th rowWalk In

Common Values

ValueCountFrequency (%)
Walk In 117658
58.0%
Ninguno 84718
41.7%
Sin Definir 492
 
0.2%
Entre Semana 92
 
< 0.1%
Lunamielero 40
 
< 0.1%
Fin De Semana 2
 
< 0.1%

Length

2025-05-11T19:30:03.685607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:03.828604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
walk 117658
36.6%
in 117658
36.6%
ninguno 84718
26.4%
sin 492
 
0.2%
definir 492
 
0.2%
semana 94
 
< 0.1%
entre 92
 
< 0.1%
lunamielero 40
 
< 0.1%
fin 2
 
< 0.1%
de 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
n 288306
20.3%
118246
8.3%
a 117886
8.3%
l 117698
8.3%
k 117658
8.3%
W 117658
8.3%
I 117658
8.3%
i 86236
 
6.1%
o 84758
 
6.0%
u 84758
 
6.0%
Other values (12) 172752
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1423614
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
n 288306
20.3%
118246
8.3%
a 117886
8.3%
l 117698
8.3%
k 117658
8.3%
W 117658
8.3%
I 117658
8.3%
i 86236
 
6.1%
o 84758
 
6.0%
u 84758
 
6.0%
Other values (12) 172752
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1423614
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
n 288306
20.3%
118246
8.3%
a 117886
8.3%
l 117698
8.3%
k 117658
8.3%
W 117658
8.3%
I 117658
8.3%
i 86236
 
6.1%
o 84758
 
6.0%
u 84758
 
6.0%
Other values (12) 172752
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1423614
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
n 288306
20.3%
118246
8.3%
a 117886
8.3%
l 117698
8.3%
k 117658
8.3%
W 117658
8.3%
I 117658
8.3%
i 86236
 
6.1%
o 84758
 
6.0%
u 84758
 
6.0%
Other values (12) 172752
12.1%

id_segmento
Categorical

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.1 KiB
17
73098 
14
27298 
5
26874 
16
16872 
12
15887 
Other values (15)
42973 

Length

Max length2
Median length2
Mean length1.7744702
Min length1

Characters and Unicode

Total characters360221
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row14
2nd row14
3rd row14
4th row14
5th row5

Common Values

ValueCountFrequency (%)
17 73098
36.0%
14 27298
 
13.4%
5 26874
 
13.2%
16 16872
 
8.3%
12 15887
 
7.8%
18 9082
 
4.5%
1 6059
 
3.0%
13 5770
 
2.8%
8 5195
 
2.6%
10 3212
 
1.6%
Other values (10) 13655
 
6.7%

Length

2025-05-11T19:30:04.024848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
17 73098
36.0%
14 27298
 
13.4%
5 26874
 
13.2%
16 16872
 
8.3%
12 15887
 
7.8%
18 9082
 
4.5%
1 6059
 
3.0%
13 5770
 
2.8%
8 5195
 
2.6%
10 3212
 
1.6%
Other values (10) 13655
 
6.7%

Most occurring characters

ValueCountFrequency (%)
1 166089
46.1%
7 75118
20.9%
4 27975
 
7.8%
5 26878
 
7.5%
6 18498
 
5.1%
2 15921
 
4.4%
8 14277
 
4.0%
9 5901
 
1.6%
3 5774
 
1.6%
0 3790
 
1.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 360221
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 166089
46.1%
7 75118
20.9%
4 27975
 
7.8%
5 26878
 
7.5%
6 18498
 
5.1%
2 15921
 
4.4%
8 14277
 
4.0%
9 5901
 
1.6%
3 5774
 
1.6%
0 3790
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 360221
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 166089
46.1%
7 75118
20.9%
4 27975
 
7.8%
5 26878
 
7.5%
6 18498
 
5.1%
2 15921
 
4.4%
8 14277
 
4.0%
9 5901
 
1.6%
3 5774
 
1.6%
0 3790
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 360221
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 166089
46.1%
7 75118
20.9%
4 27975
 
7.8%
5 26878
 
7.5%
6 18498
 
5.1%
2 15921
 
4.4%
8 14277
 
4.0%
9 5901
 
1.6%
3 5774
 
1.6%
0 3790
 
1.1%

nombre_segmento
Categorical

High correlation 

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.1 KiB
Tour Operators Domestic
73098 
Individual Ep/vac. Club
27298 
Ecommerce Ota Domestic
26874 
Individual Leisure/package
16872 
Gro. & Conv. Meetings
15887 
Other values (15)
42973 

Length

Max length29
Median length23
Mean length23.380341
Min length11

Characters and Unicode

Total characters4746256
Distinct characters38
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIndividual Ep/vac. Club
2nd rowIndividual Ep/vac. Club
3rd rowIndividual Ep/vac. Club
4th rowIndividual Ep/vac. Club
5th rowEcommerce Ota Domestic

Common Values

ValueCountFrequency (%)
Tour Operators Domestic 73098
36.0%
Individual Ep/vac. Club 27298
 
13.4%
Ecommerce Ota Domestic 26874
 
13.2%
Individual Leisure/package 16872
 
8.3%
Gro. & Conv. Meetings 15887
 
7.8%
Tour Operators Internationals 9082
 
4.5%
Complementaries Cortesias 6059
 
3.0%
Individual Business/loyalty 5770
 
2.8%
Ecommerce Website 5195
 
2.6%
Gro. & Conv. Incentive Soc. 3212
 
1.6%
Other values (10) 13655
 
6.7%

Length

2025-05-11T19:30:04.232497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
domestic 99972
16.5%
tour 85365
14.1%
operators 85365
14.1%
individual 49944
8.3%
ecommerce 35715
 
5.9%
club 29318
 
4.8%
ota 28500
 
4.7%
ep/vac 27298
 
4.5%
gro 24626
 
4.1%
24626
 
4.1%
Other values (22) 114410
18.9%

Most occurring characters

ValueCountFrequency (%)
e 416470
 
8.8%
402137
 
8.5%
o 399609
 
8.4%
r 362988
 
7.6%
i 285755
 
6.0%
s 284584
 
6.0%
t 278158
 
5.9%
a 271433
 
5.7%
c 224028
 
4.7%
u 190080
 
4.0%
Other values (28) 1631014
34.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4746256
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 416470
 
8.8%
402137
 
8.5%
o 399609
 
8.4%
r 362988
 
7.6%
i 285755
 
6.0%
s 284584
 
6.0%
t 278158
 
5.9%
a 271433
 
5.7%
c 224028
 
4.7%
u 190080
 
4.0%
Other values (28) 1631014
34.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4746256
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 416470
 
8.8%
402137
 
8.5%
o 399609
 
8.4%
r 362988
 
7.6%
i 285755
 
6.0%
s 284584
 
6.0%
t 278158
 
5.9%
a 271433
 
5.7%
c 224028
 
4.7%
u 190080
 
4.0%
Other values (28) 1631014
34.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4746256
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 416470
 
8.8%
402137
 
8.5%
o 399609
 
8.4%
r 362988
 
7.6%
i 285755
 
6.0%
s 284584
 
6.0%
t 278158
 
5.9%
a 271433
 
5.7%
c 224028
 
4.7%
u 190080
 
4.0%
Other values (28) 1631014
34.4%

id_agencia
Categorical

High cardinality 

Distinct120
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size203.4 KiB
99
31553 
112
25735 
86
13031 
14
12337 
116
 
8846
Other values (115)
111500 

Length

Max length3
Median length2
Mean length2.2083477
Min length1

Characters and Unicode

Total characters448299
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row112
2nd row112
3rd row112
4th row112
5th row14

Common Values

ValueCountFrequency (%)
99 31553
 
15.5%
112 25735
 
12.7%
86 13031
 
6.4%
14 12337
 
6.1%
116 8846
 
4.4%
33 7596
 
3.7%
93 7160
 
3.5%
89 6582
 
3.2%
113 6337
 
3.1%
32 4575
 
2.3%
Other values (110) 79250
39.0%

Length

2025-05-11T19:30:04.443804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
99 31553
 
15.5%
112 25735
 
12.7%
86 13031
 
6.4%
14 12337
 
6.1%
116 8846
 
4.4%
33 7596
 
3.7%
93 7160
 
3.5%
89 6582
 
3.2%
113 6337
 
3.1%
32 4575
 
2.3%
Other values (110) 79250
39.0%

Most occurring characters

ValueCountFrequency (%)
1 126778
28.3%
9 88800
19.8%
2 57668
12.9%
3 48962
 
10.9%
6 36482
 
8.1%
8 28336
 
6.3%
4 26026
 
5.8%
0 12477
 
2.8%
7 12359
 
2.8%
5 10411
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 448299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 126778
28.3%
9 88800
19.8%
2 57668
12.9%
3 48962
 
10.9%
6 36482
 
8.1%
8 28336
 
6.3%
4 26026
 
5.8%
0 12477
 
2.8%
7 12359
 
2.8%
5 10411
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 448299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 126778
28.3%
9 88800
19.8%
2 57668
12.9%
3 48962
 
10.9%
6 36482
 
8.1%
8 28336
 
6.3%
4 26026
 
5.8%
0 12477
 
2.8%
7 12359
 
2.8%
5 10411
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 448299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 126778
28.3%
9 88800
19.8%
2 57668
12.9%
3 48962
 
10.9%
6 36482
 
8.1%
8 28336
 
6.3%
4 26026
 
5.8%
0 12477
 
2.8%
7 12359
 
2.8%
5 10411
 
2.3%

nombre_agencia
Categorical

High cardinality 

Distinct112
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size203.3 KiB
Hoteles S.a.
42342 
Quality Services Morelia
31553 
Clientes Particulares
13031 
Bestday Travel Group
12337 
Sunwing Vacations
 
8846
Other values (107)
94893 

Length

Max length50
Median length48
Mean length20.876479
Min length3

Characters and Unicode

Total characters4237967
Distinct characters67
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHoteles S.a.
2nd rowHoteles S.a.
3rd rowHoteles S.a.
4th rowHoteles S.a.
5th rowBestday Travel Group

Common Values

ValueCountFrequency (%)
Hoteles S.a. 42342
20.9%
Quality Services Morelia 31553
15.5%
Clientes Particulares 13031
 
6.4%
Bestday Travel Group 12337
 
6.1%
Sunwing Vacations 8846
 
4.4%
Cruz Azul Corporativo 7596
 
3.7%
Quality Services Leon 7160
 
3.5%
Pricetravel Holding 6582
 
3.2%
Operadora De Viajes Solatino 6337
 
3.1%
Quality Services Aguascalientes 4195
 
2.1%
Other values (102) 63023
31.0%

Length

2025-05-11T19:30:04.683012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
services 51133
 
8.8%
quality 49167
 
8.4%
s.a 48944
 
8.4%
hoteles 42342
 
7.2%
morelia 37248
 
6.4%
de 26941
 
4.6%
group 17293
 
3.0%
travel 16163
 
2.8%
particulares 13031
 
2.2%
clientes 13031
 
2.2%
Other values (247) 268769
46.0%

Most occurring characters

ValueCountFrequency (%)
e 455974
 
10.8%
381060
 
9.0%
a 364207
 
8.6%
i 297220
 
7.0%
r 277984
 
6.6%
o 261207
 
6.2%
l 243489
 
5.7%
t 210255
 
5.0%
s 204322
 
4.8%
u 141851
 
3.3%
Other values (57) 1400398
33.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4237967
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 455974
 
10.8%
381060
 
9.0%
a 364207
 
8.6%
i 297220
 
7.0%
r 277984
 
6.6%
o 261207
 
6.2%
l 243489
 
5.7%
t 210255
 
5.0%
s 204322
 
4.8%
u 141851
 
3.3%
Other values (57) 1400398
33.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4237967
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 455974
 
10.8%
381060
 
9.0%
a 364207
 
8.6%
i 297220
 
7.0%
r 277984
 
6.6%
o 261207
 
6.2%
l 243489
 
5.7%
t 210255
 
5.0%
s 204322
 
4.8%
u 141851
 
3.3%
Other values (57) 1400398
33.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4237967
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 455974
 
10.8%
381060
 
9.0%
a 364207
 
8.6%
i 297220
 
7.0%
r 277984
 
6.6%
o 261207
 
6.2%
l 243489
 
5.7%
t 210255
 
5.0%
s 204322
 
4.8%
u 141851
 
3.3%
Other values (57) 1400398
33.0%

ciudad_agencia
Categorical

High cardinality  High correlation 

Distinct56
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size200.9 KiB
Morelia
60055 
Mexico City
37707 
Ixtapa
24175 
Cancún
18919 
Toronto
8846 
Other values (51)
53300 

Length

Max length15
Median length14
Mean length8.1781953
Min length4

Characters and Unicode

Total characters1660190
Distinct characters51
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMexico City
2nd rowMexico City
3rd rowMexico City
4th rowMexico City
5th rowCancún

Common Values

ValueCountFrequency (%)
Morelia 60055
29.6%
Mexico City 37707
18.6%
Ixtapa 24175
11.9%
Cancún 18919
 
9.3%
Toronto 8846
 
4.4%
Del. Coyoacan 8392
 
4.1%
Cancun 7583
 
3.7%
Leon 6069
 
3.0%
Zapopan 3917
 
1.9%
Cd De Mexico 2458
 
1.2%
Other values (46) 24881
12.3%

Length

2025-05-11T19:30:04.823282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
morelia 60055
22.4%
mexico 42909
16.0%
city 37733
14.0%
ixtapa 24175
9.0%
cancún 18919
 
7.0%
coyoacan 9308
 
3.5%
toronto 8846
 
3.3%
del 8652
 
3.2%
cancun 7583
 
2.8%
de 6376
 
2.4%
Other values (54) 44114
16.4%

Most occurring characters

ValueCountFrequency (%)
a 193777
11.7%
o 167958
 
10.1%
i 157340
 
9.5%
e 141709
 
8.5%
M 109373
 
6.6%
n 93606
 
5.6%
c 84015
 
5.1%
C 81939
 
4.9%
t 80242
 
4.8%
r 79335
 
4.8%
Other values (41) 470896
28.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1660190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 193777
11.7%
o 167958
 
10.1%
i 157340
 
9.5%
e 141709
 
8.5%
M 109373
 
6.6%
n 93606
 
5.6%
c 84015
 
5.1%
C 81939
 
4.9%
t 80242
 
4.8%
r 79335
 
4.8%
Other values (41) 470896
28.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1660190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 193777
11.7%
o 167958
 
10.1%
i 157340
 
9.5%
e 141709
 
8.5%
M 109373
 
6.6%
n 93606
 
5.6%
c 84015
 
5.1%
C 81939
 
4.9%
t 80242
 
4.8%
r 79335
 
4.8%
Other values (41) 470896
28.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1660190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 193777
11.7%
o 167958
 
10.1%
i 157340
 
9.5%
e 141709
 
8.5%
M 109373
 
6.6%
n 93606
 
5.6%
c 84015
 
5.1%
C 81939
 
4.9%
t 80242
 
4.8%
r 79335
 
4.8%
Other values (41) 470896
28.4%

entidad_federativa_agencia
Categorical

High correlation 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.1 KiB
Michoacán
62725 
México
51136 
Guerrero
28774 
Quintana Roo
26502 
Distrito Federal
9442 
Other values (18)
24423 

Length

Max length16
Median length15
Mean length8.731978
Min length5

Characters and Unicode

Total characters1772609
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMéxico
2nd rowMéxico
3rd rowMéxico
4th rowMéxico
5th rowQuintana Roo

Common Values

ValueCountFrequency (%)
Michoacán 62725
30.9%
México 51136
25.2%
Guerrero 28774
14.2%
Quintana Roo 26502
13.1%
Distrito Federal 9442
 
4.7%
Ontario 8868
 
4.4%
Guanajuato 6117
 
3.0%
Jalisco 5394
 
2.7%
Querétaro 1050
 
0.5%
Aguascalientes 1016
 
0.5%
Other values (13) 1978
 
1.0%

Length

2025-05-11T19:30:04.953625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
michoacán 62725
26.2%
méxico 51136
21.4%
guerrero 28774
12.0%
quintana 26502
11.1%
roo 26502
11.1%
distrito 9442
 
3.9%
federal 9442
 
3.9%
ontario 8868
 
3.7%
guanajuato 6117
 
2.6%
jalisco 5394
 
2.3%
Other values (19) 4422
 
1.8%

Most occurring characters

ValueCountFrequency (%)
o 228652
12.9%
c 183194
10.3%
i 176871
 
10.0%
a 163076
 
9.2%
n 132854
 
7.5%
r 117344
 
6.6%
M 114239
 
6.4%
e 80236
 
4.5%
u 69874
 
3.9%
h 62845
 
3.5%
Other values (30) 443424
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1772609
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 228652
12.9%
c 183194
10.3%
i 176871
 
10.0%
a 163076
 
9.2%
n 132854
 
7.5%
r 117344
 
6.6%
M 114239
 
6.4%
e 80236
 
4.5%
u 69874
 
3.9%
h 62845
 
3.5%
Other values (30) 443424
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1772609
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 228652
12.9%
c 183194
10.3%
i 176871
 
10.0%
a 163076
 
9.2%
n 132854
 
7.5%
r 117344
 
6.6%
M 114239
 
6.4%
e 80236
 
4.5%
u 69874
 
3.9%
h 62845
 
3.5%
Other values (30) 443424
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1772609
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 228652
12.9%
c 183194
10.3%
i 176871
 
10.0%
a 163076
 
9.2%
n 132854
 
7.5%
r 117344
 
6.6%
M 114239
 
6.4%
e 80236
 
4.5%
u 69874
 
3.9%
h 62845
 
3.5%
Other values (30) 443424
25.0%

pais_agencia
Categorical

High correlation  Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.6 KiB
México
192788 
Canada
 
8868
Estados Unidos
 
1072
Costa Rica
 
198
Tarifas
 
76

Length

Max length14
Median length6
Mean length6.0465217
Min length6

Characters and Unicode

Total characters1227456
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMéxico
2nd rowMéxico
3rd rowMéxico
4th rowMéxico
5th rowMéxico

Common Values

ValueCountFrequency (%)
México 192788
95.0%
Canada 8868
 
4.4%
Estados Unidos 1072
 
0.5%
Costa Rica 198
 
0.1%
Tarifas 76
 
< 0.1%

Length

2025-05-11T19:30:05.083311image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:05.174726image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
méxico 192788
94.4%
canada 8868
 
4.3%
estados 1072
 
0.5%
unidos 1072
 
0.5%
costa 198
 
0.1%
rica 198
 
0.1%
tarifas 76
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
o 195130
15.9%
i 194134
15.8%
c 192986
15.7%
x 192788
15.7%
é 192788
15.7%
M 192788
15.7%
a 28224
 
2.3%
d 11012
 
0.9%
n 9940
 
0.8%
C 9066
 
0.7%
Other values (9) 8600
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1227456
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 195130
15.9%
i 194134
15.8%
c 192986
15.7%
x 192788
15.7%
é 192788
15.7%
M 192788
15.7%
a 28224
 
2.3%
d 11012
 
0.9%
n 9940
 
0.8%
C 9066
 
0.7%
Other values (9) 8600
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1227456
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 195130
15.9%
i 194134
15.8%
c 192986
15.7%
x 192788
15.7%
é 192788
15.7%
M 192788
15.7%
a 28224
 
2.3%
d 11012
 
0.9%
n 9940
 
0.8%
C 9066
 
0.7%
Other values (9) 8600
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1227456
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 195130
15.9%
i 194134
15.8%
c 192986
15.7%
x 192788
15.7%
é 192788
15.7%
M 192788
15.7%
a 28224
 
2.3%
d 11012
 
0.9%
n 9940
 
0.8%
C 9066
 
0.7%
Other values (9) 8600
 
0.7%

id_empresa
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.5 KiB
1
203002 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters203002
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 203002
100.0%

Length

2025-05-11T19:30:05.283000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:05.344302image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 203002
100.0%

Most occurring characters

ValueCountFrequency (%)
1 203002
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 203002
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 203002
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 203002
100.0%

nombre_empresa
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.5 KiB
Hotel 1
203002 

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters1421014
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHotel 1
2nd rowHotel 1
3rd rowHotel 1
4th rowHotel 1
5th rowHotel 1

Common Values

ValueCountFrequency (%)
Hotel 1 203002
100.0%

Length

2025-05-11T19:30:05.414687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:05.479776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
hotel 203002
50.0%
1 203002
50.0%

Most occurring characters

ValueCountFrequency (%)
H 203002
14.3%
o 203002
14.3%
t 203002
14.3%
e 203002
14.3%
l 203002
14.3%
203002
14.3%
1 203002
14.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1421014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
H 203002
14.3%
o 203002
14.3%
t 203002
14.3%
e 203002
14.3%
l 203002
14.3%
203002
14.3%
1 203002
14.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1421014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
H 203002
14.3%
o 203002
14.3%
t 203002
14.3%
e 203002
14.3%
l 203002
14.3%
203002
14.3%
1 203002
14.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1421014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
H 203002
14.3%
o 203002
14.3%
t 203002
14.3%
e 203002
14.3%
l 203002
14.3%
203002
14.3%
1 203002
14.3%

total_habitaciones_empresa
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.5 MiB
735
203002 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters609006
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row735
2nd row735
3rd row735
4th row735
5th row735

Common Values

ValueCountFrequency (%)
735 203002
100.0%

Length

2025-05-11T19:30:05.555987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:05.621128image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
735 203002
100.0%

Most occurring characters

ValueCountFrequency (%)
7 203002
33.3%
3 203002
33.3%
5 203002
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 609006
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
7 203002
33.3%
3 203002
33.3%
5 203002
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 609006
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
7 203002
33.3%
3 203002
33.3%
5 203002
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 609006
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
7 203002
33.3%
3 203002
33.3%
5 203002
33.3%

id_tipo_habitacion
Categorical

High correlation 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.6 KiB
4
52796 
19
51281 
17
30868 
6
13910 
25
11124 
Other values (23)
43023 

Length

Max length2
Median length2
Mean length1.6264323
Min length1

Characters and Unicode

Total characters330169
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row25
2nd row25
3rd row23
4th row24
5th row4

Common Values

ValueCountFrequency (%)
4 52796
26.0%
19 51281
25.3%
17 30868
15.2%
6 13910
 
6.9%
25 11124
 
5.5%
13 10510
 
5.2%
5 6091
 
3.0%
23 5793
 
2.9%
26 4909
 
2.4%
24 3849
 
1.9%
Other values (18) 11871
 
5.8%

Length

2025-05-11T19:30:05.729260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
4 52796
26.0%
19 51281
25.3%
17 30868
15.2%
6 13910
 
6.9%
25 11124
 
5.5%
13 10510
 
5.2%
5 6091
 
3.0%
23 5793
 
2.9%
26 4909
 
2.4%
24 3849
 
1.9%
Other values (18) 11871
 
5.8%

Most occurring characters

ValueCountFrequency (%)
1 98405
29.8%
4 56983
17.3%
9 51417
15.6%
7 34996
 
10.6%
2 33669
 
10.2%
6 18885
 
5.7%
5 17365
 
5.3%
3 16493
 
5.0%
8 1446
 
0.4%
0 510
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 330169
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 98405
29.8%
4 56983
17.3%
9 51417
15.6%
7 34996
 
10.6%
2 33669
 
10.2%
6 18885
 
5.7%
5 17365
 
5.3%
3 16493
 
5.0%
8 1446
 
0.4%
0 510
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 330169
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 98405
29.8%
4 56983
17.3%
9 51417
15.6%
7 34996
 
10.6%
2 33669
 
10.2%
6 18885
 
5.7%
5 17365
 
5.3%
3 16493
 
5.0%
8 1446
 
0.4%
0 510
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 330169
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 98405
29.8%
4 56983
17.3%
9 51417
15.6%
7 34996
 
10.6%
2 33669
 
10.2%
6 18885
 
5.7%
5 17365
 
5.3%
3 16493
 
5.0%
8 1446
 
0.4%
0 510
 
0.2%

nombre_tipo_habitacion
Categorical

High correlation 

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.6 KiB
Luxury 2q Sn12gsu
52796 
Estd Db Sn12ast
51281 
Estd C/balcon Sn12asb
30868 
Luxury 1k Sn12gsu
13910 
Estd 2q Sn12mst
11124 
Other values (23)
43023 

Length

Max length22
Median length21
Mean length17.6079
Min length11

Characters and Unicode

Total characters3574439
Distinct characters42
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEstd 2q Sn12mst
2nd rowEstd 2q Sn12mst
3rd rowJr Suite 1k Sn12mjs
4th rowJr Suite 2q Sn12mjs
5th rowLuxury 2q Sn12gsu

Common Values

ValueCountFrequency (%)
Luxury 2q Sn12gsu 52796
26.0%
Estd Db Sn12ast 51281
25.3%
Estd C/balcon Sn12asb 30868
15.2%
Luxury 1k Sn12gsu 13910
 
6.9%
Estd 2q Sn12mst 11124
 
5.5%
Sup Luj King Sn12asb 10510
 
5.2%
Luxury 2q Sb Sn12gsu 6091
 
3.0%
Jr Suite 1k Sn12mjs 5793
 
2.9%
Mv Luxury 2q Sn12gsu 4909
 
2.4%
Jr Suite 2q Sn12mjs 3849
 
1.9%
Other values (18) 11871
 
5.8%

Length

2025-05-11T19:30:05.857132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
estd 96111
14.9%
sn12gsu 80304
12.5%
luxury 79682
12.4%
2q 78998
12.3%
db 51805
8.1%
sn12ast 51281
8.0%
sn12asb 43060
6.7%
c/balcon 30868
 
4.8%
1k 21882
 
3.4%
suite 15729
 
2.4%
Other values (28) 93478
14.5%

Most occurring characters

ValueCountFrequency (%)
440196
 
12.3%
s 303642
 
8.5%
2 281990
 
7.9%
u 277429
 
7.8%
n 246696
 
6.9%
S 235266
 
6.6%
1 227026
 
6.4%
t 176397
 
4.9%
a 140160
 
3.9%
b 132162
 
3.7%
Other values (32) 1113475
31.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3574439
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
440196
 
12.3%
s 303642
 
8.5%
2 281990
 
7.9%
u 277429
 
7.8%
n 246696
 
6.9%
S 235266
 
6.6%
1 227026
 
6.4%
t 176397
 
4.9%
a 140160
 
3.9%
b 132162
 
3.7%
Other values (32) 1113475
31.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3574439
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
440196
 
12.3%
s 303642
 
8.5%
2 281990
 
7.9%
u 277429
 
7.8%
n 246696
 
6.9%
S 235266
 
6.6%
1 227026
 
6.4%
t 176397
 
4.9%
a 140160
 
3.9%
b 132162
 
3.7%
Other values (32) 1113475
31.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3574439
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
440196
 
12.3%
s 303642
 
8.5%
2 281990
 
7.9%
u 277429
 
7.8%
n 246696
 
6.9%
S 235266
 
6.6%
1 227026
 
6.4%
t 176397
 
4.9%
a 140160
 
3.9%
b 132162
 
3.7%
Other values (32) 1113475
31.2%

cupo_tipo_habitacion
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size1.5 MiB
2.0
202992 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608976
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 202992
> 99.9%
(Missing) 10
 
< 0.1%

Length

2025-05-11T19:30:05.973450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:06.036101image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 202992
100.0%

Most occurring characters

ValueCountFrequency (%)
2 202992
33.3%
. 202992
33.3%
0 202992
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 202992
33.3%
. 202992
33.3%
0 202992
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 202992
33.3%
. 202992
33.3%
0 202992
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 202992
33.3%
. 202992
33.3%
0 202992
33.3%

clasificacion_tipo_habitacion
Categorical

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing10
Missing (%)< 0.1%
Memory size198.8 KiB
Gsu
80304 
Ast
51281 
Asb
43398 
Mst
11124 
Mjs
9642 
Other values (6)
 
7243

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters608976
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMst
2nd rowMst
3rd rowMjs
4th rowMjs
5th rowGsu

Common Values

ValueCountFrequency (%)
Gsu 80304
39.6%
Ast 51281
25.3%
Asb 43398
21.4%
Mst 11124
 
5.5%
Mjs 9642
 
4.7%
Gms 2152
 
1.1%
Ajs 1808
 
0.9%
Afm 1793
 
0.9%
Asd 1156
 
0.6%
Asp 216
 
0.1%

Length

2025-05-11T19:30:06.110124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
gsu 80304
39.6%
ast 51281
25.3%
asb 43398
21.4%
mst 11124
 
5.5%
mjs 9642
 
4.7%
gms 2152
 
1.1%
ajs 1808
 
0.9%
afm 1793
 
0.9%
asd 1156
 
0.6%
asp 216
 
0.1%

Most occurring characters

ValueCountFrequency (%)
s 201199
33.0%
A 99652
16.4%
G 82574
13.6%
u 80304
 
13.2%
t 62405
 
10.2%
b 43398
 
7.1%
M 20766
 
3.4%
j 11450
 
1.9%
m 3945
 
0.6%
f 1793
 
0.3%
Other values (2) 1490
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 608976
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 201199
33.0%
A 99652
16.4%
G 82574
13.6%
u 80304
 
13.2%
t 62405
 
10.2%
b 43398
 
7.1%
M 20766
 
3.4%
j 11450
 
1.9%
m 3945
 
0.6%
f 1793
 
0.3%
Other values (2) 1490
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 608976
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 201199
33.0%
A 99652
16.4%
G 82574
13.6%
u 80304
 
13.2%
t 62405
 
10.2%
b 43398
 
7.1%
M 20766
 
3.4%
j 11450
 
1.9%
m 3945
 
0.6%
f 1793
 
0.3%
Other values (2) 1490
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 608976
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 201199
33.0%
A 99652
16.4%
G 82574
13.6%
u 80304
 
13.2%
t 62405
 
10.2%
b 43398
 
7.1%
M 20766
 
3.4%
j 11450
 
1.9%
m 3945
 
0.6%
f 1793
 
0.3%
Other values (2) 1490
 
0.2%

id_canal
Categorical

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.0 KiB
1
82255 
4
80877 
10
19743 
7
 
6805
0
 
6378
Other values (9)
 
6944

Length

Max length2
Median length1
Mean length1.1086393
Min length1

Characters and Unicode

Total characters225056
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10
2nd row10
3rd row10
4th row10
5th row13

Common Values

ValueCountFrequency (%)
1 82255
40.5%
4 80877
39.8%
10 19743
 
9.7%
7 6805
 
3.4%
0 6378
 
3.1%
8 2225
 
1.1%
13 1960
 
1.0%
9 1092
 
0.5%
5 896
 
0.4%
11 347
 
0.2%
Other values (4) 424
 
0.2%

Length

2025-05-11T19:30:06.222348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1 82255
40.5%
4 80877
39.8%
10 19743
 
9.7%
7 6805
 
3.4%
0 6378
 
3.1%
8 2225
 
1.1%
13 1960
 
1.0%
9 1092
 
0.5%
5 896
 
0.4%
11 347
 
0.2%
Other values (4) 424
 
0.2%

Most occurring characters

ValueCountFrequency (%)
1 104656
46.5%
4 80877
35.9%
0 26121
 
11.6%
7 6805
 
3.0%
8 2225
 
1.0%
3 2120
 
0.9%
9 1092
 
0.5%
5 896
 
0.4%
2 144
 
0.1%
6 120
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 225056
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 104656
46.5%
4 80877
35.9%
0 26121
 
11.6%
7 6805
 
3.0%
8 2225
 
1.0%
3 2120
 
0.9%
9 1092
 
0.5%
5 896
 
0.4%
2 144
 
0.1%
6 120
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 225056
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 104656
46.5%
4 80877
35.9%
0 26121
 
11.6%
7 6805
 
3.0%
8 2225
 
1.0%
3 2120
 
0.9%
9 1092
 
0.5%
5 896
 
0.4%
2 144
 
0.1%
6 120
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 225056
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 104656
46.5%
4 80877
35.9%
0 26121
 
11.6%
7 6805
 
3.0%
8 2225
 
1.0%
3 2120
 
0.9%
9 1092
 
0.5%
5 896
 
0.4%
2 144
 
0.1%
6 120
 
0.1%

nombre_canal
Categorical

High correlation 

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.0 KiB
Lada 800 Nacional68
82255 
Directo
80877 
Multivacaciones 2
19743 
Internet
 
6805
Sin Definir
 
6378
Other values (9)
 
6944

Length

Max length22
Median length19
Mean length13.119925
Min length3

Characters and Unicode

Total characters2663371
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMultivacaciones 2
2nd rowMultivacaciones 2
3rd rowMultivacaciones 2
4th rowMultivacaciones 2
5th rowVertical Booking

Common Values

ValueCountFrequency (%)
Lada 800 Nacional68 82255
40.5%
Directo 80877
39.8%
Multivacaciones 2 19743
 
9.7%
Internet 6805
 
3.4%
Sin Definir 6378
 
3.1%
Fax 2225
 
1.1%
Vertical Booking 1960
 
1.0%
Directo Hotel 1092
 
0.5%
Conmutador 896
 
0.4%
Multivacaciones 1 347
 
0.2%
Other values (4) 424
 
0.2%

Length

2025-05-11T19:30:06.339872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lada 82555
20.8%
800 82555
20.8%
nacional68 82255
20.7%
directo 81969
20.6%
multivacaciones 20090
 
5.1%
2 19743
 
5.0%
internet 6805
 
1.7%
sin 6378
 
1.6%
definir 6378
 
1.6%
fax 2225
 
0.6%
Other values (10) 6683
 
1.7%

Most occurring characters

ValueCountFrequency (%)
a 375481
14.1%
i 227890
 
8.6%
c 206664
 
7.8%
194634
 
7.3%
o 191550
 
7.2%
0 165250
 
6.2%
8 164810
 
6.2%
n 132427
 
5.0%
e 125259
 
4.7%
t 119781
 
4.5%
Other values (27) 759625
28.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2663371
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 375481
14.1%
i 227890
 
8.6%
c 206664
 
7.8%
194634
 
7.3%
o 191550
 
7.2%
0 165250
 
6.2%
8 164810
 
6.2%
n 132427
 
5.0%
e 125259
 
4.7%
t 119781
 
4.5%
Other values (27) 759625
28.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2663371
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 375481
14.1%
i 227890
 
8.6%
c 206664
 
7.8%
194634
 
7.3%
o 191550
 
7.2%
0 165250
 
6.2%
8 164810
 
6.2%
n 132427
 
5.0%
e 125259
 
4.7%
t 119781
 
4.5%
Other values (27) 759625
28.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2663371
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 375481
14.1%
i 227890
 
8.6%
c 206664
 
7.8%
194634
 
7.3%
o 191550
 
7.2%
0 165250
 
6.2%
8 164810
 
6.2%
n 132427
 
5.0%
e 125259
 
4.7%
t 119781
 
4.5%
Other values (27) 759625
28.5%

id_pais_origen
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.6 KiB
157
201504 
0
 
646
232
 
624
38
 
228

Length

Max length3
Median length3
Mean length2.9925124
Min length1

Characters and Unicode

Total characters607486
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row157
2nd row157
3rd row157
4th row157
5th row157

Common Values

ValueCountFrequency (%)
157 201504
99.3%
0 646
 
0.3%
232 624
 
0.3%
38 228
 
0.1%

Length

2025-05-11T19:30:06.453645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:06.529657image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
157 201504
99.3%
0 646
 
0.3%
232 624
 
0.3%
38 228
 
0.1%

Most occurring characters

ValueCountFrequency (%)
1 201504
33.2%
5 201504
33.2%
7 201504
33.2%
2 1248
 
0.2%
3 852
 
0.1%
0 646
 
0.1%
8 228
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 607486
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 201504
33.2%
5 201504
33.2%
7 201504
33.2%
2 1248
 
0.2%
3 852
 
0.1%
0 646
 
0.1%
8 228
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 607486
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 201504
33.2%
5 201504
33.2%
7 201504
33.2%
2 1248
 
0.2%
3 852
 
0.1%
0 646
 
0.1%
8 228
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 607486
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 201504
33.2%
5 201504
33.2%
7 201504
33.2%
2 1248
 
0.2%
3 852
 
0.1%
0 646
 
0.1%
8 228
 
< 0.1%

nombre_pais_origen
Categorical

High correlation  Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.6 KiB
Mexico
201504 
Sin Definir
 
646
United States
 
624
Canada
 
228

Length

Max length13
Median length6
Mean length6.0374282
Min length6

Characters and Unicode

Total characters1225610
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMexico
2nd rowMexico
3rd rowMexico
4th rowMexico
5th rowMexico

Common Values

ValueCountFrequency (%)
Mexico 201504
99.3%
Sin Definir 646
 
0.3%
United States 624
 
0.3%
Canada 228
 
0.1%

Length

2025-05-11T19:30:06.625594image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:06.700005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
mexico 201504
98.6%
sin 646
 
0.3%
definir 646
 
0.3%
united 624
 
0.3%
states 624
 
0.3%
canada 228
 
0.1%

Most occurring characters

ValueCountFrequency (%)
i 204066
16.7%
e 203398
16.6%
M 201504
16.4%
x 201504
16.4%
c 201504
16.4%
o 201504
16.4%
n 2144
 
0.2%
t 1872
 
0.2%
a 1308
 
0.1%
S 1270
 
0.1%
Other values (8) 5536
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1225610
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
i 204066
16.7%
e 203398
16.6%
M 201504
16.4%
x 201504
16.4%
c 201504
16.4%
o 201504
16.4%
n 2144
 
0.2%
t 1872
 
0.2%
a 1308
 
0.1%
S 1270
 
0.1%
Other values (8) 5536
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1225610
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
i 204066
16.7%
e 203398
16.6%
M 201504
16.4%
x 201504
16.4%
c 201504
16.4%
o 201504
16.4%
n 2144
 
0.2%
t 1872
 
0.2%
a 1308
 
0.1%
S 1270
 
0.1%
Other values (8) 5536
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1225610
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
i 204066
16.7%
e 203398
16.6%
M 201504
16.4%
x 201504
16.4%
c 201504
16.4%
o 201504
16.4%
n 2144
 
0.2%
t 1872
 
0.2%
a 1308
 
0.1%
S 1270
 
0.1%
Other values (8) 5536
 
0.5%

reservacion_pendiente
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.4 KiB
True
102199 
False
100803 
ValueCountFrequency (%)
True 102199
50.3%
False 100803
49.7%
2025-05-11T19:30:06.779624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

id_estatus_reservacion
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.7 KiB
9
131069 
2
33301 
5
18880 
1
17158 
3
 
1854
Other values (3)
 
740

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters203002
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9
2nd row9
3rd row9
4th row9
5th row9

Common Values

ValueCountFrequency (%)
9 131069
64.6%
2 33301
 
16.4%
5 18880
 
9.3%
1 17158
 
8.5%
3 1854
 
0.9%
4 586
 
0.3%
8 130
 
0.1%
7 24
 
< 0.1%

Length

2025-05-11T19:30:06.861771image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:06.946273image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
9 131069
64.6%
2 33301
 
16.4%
5 18880
 
9.3%
1 17158
 
8.5%
3 1854
 
0.9%
4 586
 
0.3%
8 130
 
0.1%
7 24
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
9 131069
64.6%
2 33301
 
16.4%
5 18880
 
9.3%
1 17158
 
8.5%
3 1854
 
0.9%
4 586
 
0.3%
8 130
 
0.1%
7 24
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
9 131069
64.6%
2 33301
 
16.4%
5 18880
 
9.3%
1 17158
 
8.5%
3 1854
 
0.9%
4 586
 
0.3%
8 130
 
0.1%
7 24
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
9 131069
64.6%
2 33301
 
16.4%
5 18880
 
9.3%
1 17158
 
8.5%
3 1854
 
0.9%
4 586
 
0.3%
8 130
 
0.1%
7 24
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
9 131069
64.6%
2 33301
 
16.4%
5 18880
 
9.3%
1 17158
 
8.5%
3 1854
 
0.9%
4 586
 
0.3%
8 130
 
0.1%
7 24
 
< 0.1%

nombre_estatus_reservacion
Categorical

High correlation 

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.7 KiB
Salida
131069 
Reservacion Cancelada
33301 
Rooming List
18880 
Reservacion O (r)registro
17158 
No Show
 
1854
Other values (3)
 
740

Length

Max length25
Median length6
Mean length10.696826
Min length6

Characters and Unicode

Total characters2171477
Distinct characters28
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSalida
2nd rowSalida
3rd rowSalida
4th rowSalida
5th rowSalida

Common Values

ValueCountFrequency (%)
Salida 131069
64.6%
Reservacion Cancelada 33301
 
16.4%
Rooming List 18880
 
9.3%
Reservacion O (r)registro 17158
 
8.5%
No Show 1854
 
0.9%
Reservacion En Transicion 586
 
0.3%
En Casa (registro) 130
 
0.1%
Preregistro 24
 
< 0.1%

Length

2025-05-11T19:30:07.078089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:07.183814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
salida 131069
44.8%
reservacion 51045
 
17.4%
cancelada 33301
 
11.4%
rooming 18880
 
6.4%
list 18880
 
6.4%
o 17158
 
5.9%
r)registro 17158
 
5.9%
no 1854
 
0.6%
show 1854
 
0.6%
en 716
 
0.2%
Other values (4) 870
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 413932
19.1%
i 238358
11.0%
l 164370
 
7.6%
d 164370
 
7.6%
e 152727
 
7.0%
S 132923
 
6.1%
o 110411
 
5.1%
n 105114
 
4.8%
r 103437
 
4.8%
89783
 
4.1%
Other values (18) 496052
22.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2171477
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 413932
19.1%
i 238358
11.0%
l 164370
 
7.6%
d 164370
 
7.6%
e 152727
 
7.0%
S 132923
 
6.1%
o 110411
 
5.1%
n 105114
 
4.8%
r 103437
 
4.8%
89783
 
4.1%
Other values (18) 496052
22.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2171477
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 413932
19.1%
i 238358
11.0%
l 164370
 
7.6%
d 164370
 
7.6%
e 152727
 
7.0%
S 132923
 
6.1%
o 110411
 
5.1%
n 105114
 
4.8%
r 103437
 
4.8%
89783
 
4.1%
Other values (18) 496052
22.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2171477
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 413932
19.1%
i 238358
11.0%
l 164370
 
7.6%
d 164370
 
7.6%
e 152727
 
7.0%
S 132923
 
6.1%
o 110411
 
5.1%
n 105114
 
4.8%
r 103437
 
4.8%
89783
 
4.1%
Other values (18) 496052
22.8%

clave_estado
Categorical

High cardinality  Imbalance 

Distinct148
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size401.8 KiB
Egr
62426 
Emc
55473 
Emx
18770 
Egt
17465 
Edf
9142 
Other values (143)
39726 

Length

Max length4
Median length3
Mean length3.0177831
Min length0

Characters and Unicode

Total characters612616
Distinct characters31
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEmx
2nd rowEgt
3rd rowEmc
4th rowEgt
5th rowEmx

Common Values

ValueCountFrequency (%)
Egr 62426
30.8%
Emc 55473
27.3%
Emx 18770
 
9.2%
Egt 17465
 
8.6%
Edf 9142
 
4.5%
Eqe 5596
 
2.8%
Ehg 5317
 
2.6%
Eagu 3164
 
1.6%
Eqr 2775
 
1.4%
Eca 2613
 
1.3%
Other values (138) 20261
 
10.0%

Length

2025-05-11T19:30:07.320629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
egr 62426
30.8%
emc 55473
27.3%
emx 18770
 
9.2%
egt 17465
 
8.6%
edf 9142
 
4.5%
eqe 5596
 
2.8%
ehg 5317
 
2.6%
eagu 3164
 
1.6%
eqr 2775
 
1.4%
eca 2613
 
1.3%
Other values (137) 20259
 
10.0%

Most occurring characters

ValueCountFrequency (%)
E 203000
33.1%
g 88648
14.5%
m 75932
 
12.4%
r 65819
 
10.7%
c 61412
 
10.0%
x 19154
 
3.1%
t 18511
 
3.0%
a 11317
 
1.8%
d 9316
 
1.5%
f 9276
 
1.5%
Other values (21) 50231
 
8.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 612616
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E 203000
33.1%
g 88648
14.5%
m 75932
 
12.4%
r 65819
 
10.7%
c 61412
 
10.0%
x 19154
 
3.1%
t 18511
 
3.0%
a 11317
 
1.8%
d 9316
 
1.5%
f 9276
 
1.5%
Other values (21) 50231
 
8.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 612616
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E 203000
33.1%
g 88648
14.5%
m 75932
 
12.4%
r 65819
 
10.7%
c 61412
 
10.0%
x 19154
 
3.1%
t 18511
 
3.0%
a 11317
 
1.8%
d 9316
 
1.5%
f 9276
 
1.5%
Other values (21) 50231
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 612616
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E 203000
33.1%
g 88648
14.5%
m 75932
 
12.4%
r 65819
 
10.7%
c 61412
 
10.0%
x 19154
 
3.1%
t 18511
 
3.0%
a 11317
 
1.8%
d 9316
 
1.5%
f 9276
 
1.5%
Other values (21) 50231
 
8.2%

nombre_estado
Categorical

High cardinality  Imbalance 

Distinct144
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Memory size401.8 KiB
Guerrero
62426 
Michoacán
55473 
México
18770 
Guanajuato
17465 
Distrito Federal
9142 
Other values (139)
39724 

Length

Max length30
Median length27
Mean length8.9514631
Min length4

Characters and Unicode

Total characters1817147
Distinct characters57
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMéxico
2nd rowGuanajuato
3rd rowMichoacán
4th rowGuanajuato
5th rowMéxico

Common Values

ValueCountFrequency (%)
Guerrero 62426
30.8%
Michoacán 55473
27.3%
México 18770
 
9.2%
Guanajuato 17465
 
8.6%
Distrito Federal 9142
 
4.5%
Querétaro 5596
 
2.8%
Hidalgo 5317
 
2.6%
Aguascalientes 3164
 
1.6%
Quintana Roo 2775
 
1.4%
California 2613
 
1.3%
Other values (134) 20259
 
10.0%

Length

2025-05-11T19:30:07.436379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
guerrero 62426
28.0%
michoacán 55473
24.9%
méxico 18770
 
8.4%
guanajuato 17465
 
7.8%
distrito 9152
 
4.1%
federal 9142
 
4.1%
querétaro 5596
 
2.5%
hidalgo 5317
 
2.4%
aguascalientes 3164
 
1.4%
quintana 2775
 
1.2%
Other values (173) 33406
15.0%

Most occurring characters

ValueCountFrequency (%)
r 228601
12.6%
o 198703
10.9%
a 169367
 
9.3%
e 163094
 
9.0%
c 137622
 
7.6%
i 127571
 
7.0%
u 117554
 
6.5%
n 94244
 
5.2%
G 80121
 
4.4%
M 75826
 
4.2%
Other values (47) 424444
23.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1817147
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 228601
12.6%
o 198703
10.9%
a 169367
 
9.3%
e 163094
 
9.0%
c 137622
 
7.6%
i 127571
 
7.0%
u 117554
 
6.5%
n 94244
 
5.2%
G 80121
 
4.4%
M 75826
 
4.2%
Other values (47) 424444
23.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1817147
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 228601
12.6%
o 198703
10.9%
a 169367
 
9.3%
e 163094
 
9.0%
c 137622
 
7.6%
i 127571
 
7.0%
u 117554
 
6.5%
n 94244
 
5.2%
G 80121
 
4.4%
M 75826
 
4.2%
Other values (47) 424444
23.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1817147
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 228601
12.6%
o 198703
10.9%
a 169367
 
9.3%
e 163094
 
9.0%
c 137622
 
7.6%
i 127571
 
7.0%
u 117554
 
6.5%
n 94244
 
5.2%
G 80121
 
4.4%
M 75826
 
4.2%
Other values (47) 424444
23.4%

total_tarifa
Real number (ℝ)

High correlation  Skewed  Zeros 

Distinct11596
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4106.7283
Minimum-30910
Maximum1033056
Zeros106391
Zeros (%)52.4%
Negative33
Negative (%)< 0.1%
Memory size1.5 MiB
2025-05-11T19:30:07.557688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-30910
5-th percentile0
Q10
median0
Q37592
95-th percentile14720
Maximum1033056
Range1063966
Interquartile range (IQR)7592

Descriptive statistics

Standard deviation6501.7072
Coefficient of variation (CV)1.5831841
Kurtosis3619.6509
Mean4106.7283
Median Absolute Deviation (MAD)0
Skewness26.894022
Sum8.3367407 × 108
Variance42272197
MonotonicityNot monotonic
2025-05-11T19:30:07.703079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 106391
52.4%
5392 1474
 
0.7%
7236 1283
 
0.6%
8088 900
 
0.4%
7662 870
 
0.4%
7960 859
 
0.4%
8514 803
 
0.4%
5108 800
 
0.4%
6810 796
 
0.4%
7878 635
 
0.3%
Other values (11586) 88191
43.4%
ValueCountFrequency (%)
-30910 7
 
< 0.1%
-29950 1
 
< 0.1%
-20000 7
 
< 0.1%
-3087 16
 
< 0.1%
-1345.6 1
 
< 0.1%
-3 1
 
< 0.1%
0 106391
52.4%
0.08 3
 
< 0.1%
0.09 2
 
< 0.1%
0.14 4
 
< 0.1%
ValueCountFrequency (%)
1033056 1
 
< 0.1%
655848 1
 
< 0.1%
268632 1
 
< 0.1%
201474 1
 
< 0.1%
166296 1
 
< 0.1%
145544 1
 
< 0.1%
132946.8 7
< 0.1%
122500 1
 
< 0.1%
105000 1
 
< 0.1%
102816 1
 
< 0.1%

id_moneda
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.5 KiB
1
203002 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters203002
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 203002
100.0%

Length

2025-05-11T19:30:07.834685image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-11T19:30:07.890645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1 203002
100.0%

Most occurring characters

ValueCountFrequency (%)
1 203002
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 203002
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 203002
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203002
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 203002
100.0%
Distinct520
Distinct (%)0.3%
Missing497
Missing (%)0.2%
Memory size1.5 MiB
Minimum2019-01-02 00:00:00
Maximum2020-12-07 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-05-11T19:30:07.973307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:30:08.135034image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

reservacion
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.4 KiB
True
102199 
False
100803 
ValueCountFrequency (%)
True 102199
50.3%
False 100803
49.7%
2025-05-11T19:30:08.237480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

reservacion_anio_anterior
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size198.4 KiB
False
102199 
True
100803 
ValueCountFrequency (%)
False 102199
50.3%
True 100803
49.7%
2025-05-11T19:30:08.290905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

id_cliente_disp
Categorical

High correlation  Imbalance 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size199.0 KiB
0
100803 
2
80295 
3
12016 
1
 
5654
4
 
2962
Other values (11)
 
1272

Length

Max length2
Median length1
Mean length1.0004433
Min length1

Characters and Unicode

Total characters203092
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row4
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
0 100803
49.7%
2 80295
39.6%
3 12016
 
5.9%
1 5654
 
2.8%
4 2962
 
1.5%
5 536
 
0.3%
6 427
 
0.2%
8 156
 
0.1%
7 43
 
< 0.1%
15 24
 
< 0.1%
Other values (6) 86
 
< 0.1%

Length

2025-05-11T19:30:08.377088image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 100803
49.7%
2 80295
39.6%
3 12016
 
5.9%
1 5654
 
2.8%
4 2962
 
1.5%
5 536
 
0.3%
6 427
 
0.2%
8 156
 
0.1%
7 43
 
< 0.1%
15 24
 
< 0.1%
Other values (6) 86
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 100826
49.6%
2 80317
39.5%
3 12020
 
5.9%
1 5753
 
2.8%
4 2970
 
1.5%
5 560
 
0.3%
6 427
 
0.2%
8 156
 
0.1%
7 43
 
< 0.1%
9 20
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 203092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 100826
49.6%
2 80317
39.5%
3 12020
 
5.9%
1 5753
 
2.8%
4 2970
 
1.5%
5 560
 
0.3%
6 427
 
0.2%
8 156
 
0.1%
7 43
 
< 0.1%
9 20
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 203092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 100826
49.6%
2 80317
39.5%
3 12020
 
5.9%
1 5753
 
2.8%
4 2970
 
1.5%
5 560
 
0.3%
6 427
 
0.2%
8 156
 
0.1%
7 43
 
< 0.1%
9 20
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 203092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 100826
49.6%
2 80317
39.5%
3 12020
 
5.9%
1 5753
 
2.8%
4 2970
 
1.5%
5 560
 
0.3%
6 427
 
0.2%
8 156
 
0.1%
7 43
 
< 0.1%
9 20
 
< 0.1%

cliente_disp_anio_anterior
Real number (ℝ)

High correlation  Zeros 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0796938
Minimum0
Maximum15
Zeros102199
Zeros (%)50.3%
Negative0
Negative (%)0.0%
Memory size1.5 MiB
2025-05-11T19:30:08.464733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.2046937
Coefficient of variation (CV)1.1157735
Kurtosis4.2151962
Mean1.0796938
Median Absolute Deviation (MAD)0
Skewness1.0595136
Sum219180
Variance1.451287
MonotonicityNot monotonic
2025-05-11T19:30:08.560031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 102199
50.3%
2 79038
38.9%
3 11946
 
5.9%
1 5638
 
2.8%
4 2933
 
1.4%
5 534
 
0.3%
6 422
 
0.2%
8 143
 
0.1%
7 43
 
< 0.1%
10 23
 
< 0.1%
Other values (6) 83
 
< 0.1%
ValueCountFrequency (%)
0 102199
50.3%
1 5638
 
2.8%
2 79038
38.9%
3 11946
 
5.9%
4 2933
 
1.4%
5 534
 
0.3%
6 422
 
0.2%
7 43
 
< 0.1%
8 143
 
0.1%
9 19
 
< 0.1%
ValueCountFrequency (%)
15 23
 
< 0.1%
14 8
 
< 0.1%
13 4
 
< 0.1%
12 20
 
< 0.1%
11 9
 
< 0.1%
10 23
 
< 0.1%
9 19
 
< 0.1%
8 143
 
0.1%
7 43
 
< 0.1%
6 422
0.2%

Interactions

2025-05-11T19:29:53.037791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:26.790638image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:28.614010image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:30.512925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:32.384928image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:35.590975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:38.162402image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:40.217945image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:42.392103image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:44.301049image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:46.196789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:47.892946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:50.960839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:53.209132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-11T19:29:30.672443image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:32.525426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-11T19:29:44.455686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-11T19:29:51.162620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-11T19:29:46.458446image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-11T19:29:29.015993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-11T19:29:40.628160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:42.829188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:44.764725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:46.580406image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:48.336736image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:51.561595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:53.640256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:27.357616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:29.150347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:31.134534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:34.252855image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:36.355980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:38.987134image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:40.756550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:42.975678image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:44.919435image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:46.717366image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:48.474520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:51.758814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:53.783075image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:27.494301image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:29.302748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:31.281396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-11T19:29:36.560544image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-05-11T19:29:35.292718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:37.759680image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:39.949804image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:42.128858image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:44.013432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:45.924893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:47.605124image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:50.570328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:52.761256image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:54.712481image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:28.474336image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:30.360329image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:32.249578image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:35.434539image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:37.955885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:40.079843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:42.259725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:44.161673image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:46.054718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:47.736811image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:50.765649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-11T19:29:52.892303image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-11T19:30:08.706431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ciudad_agenciaclasificacion_tipo_habitacioncliente_disp_anio_anteriorentidad_federativa_agenciaid_canalid_cliente_dispid_estatus_reservacionid_pais_origenid_paqueteid_programaid_reservacionesid_segmentoid_tipo_habitacionnombre_canalnombre_estatus_reservacionnombre_pais_origennombre_paquetenombre_programanombre_segmentonombre_tipo_habitacionnumero_adultosnumero_adultos_anio_anteriornumero_menoresnumero_menores_anio_anteriornumero_nochesnumero_noches_anio_anteriornumero_personasnumero_personas_anio_anteriorpais_agenciareservacionreservacion_anio_anteriorreservacion_pendientetotal_habitacionestotal_habitaciones_anio_anteriortotal_tarifa
ciudad_agencia1.0000.2270.0970.9960.3320.0860.2020.1280.3330.4180.1280.5520.1610.3320.2020.1280.3330.4180.5520.1610.0980.0970.0230.0230.0000.0000.0910.0901.0000.0000.0000.0000.0390.0380.000
clasificacion_tipo_habitacion0.2271.0000.1110.1820.2100.1130.1680.0240.0420.0590.0590.2521.0000.2100.1680.0240.0420.0590.2521.0000.1120.1110.0320.0320.0000.0000.1070.1070.0580.0000.0000.0000.0460.0460.015
cliente_disp_anio_anterior0.0970.1111.0000.0800.0830.3150.0350.0000.0190.0050.8330.1040.1170.0830.0350.0000.0190.0050.1040.117-0.9211.000-0.1450.170-0.8920.901-0.9110.9820.0200.9460.9460.946-0.9570.958-0.849
entidad_federativa_agencia0.9960.1820.0801.0000.2750.0710.1420.1220.2410.1120.0820.4400.1430.2750.1420.1220.2410.1120.4400.1430.0810.0800.0170.0170.0000.0000.0710.0711.0000.0000.0000.0000.0330.0320.000
id_canal0.3320.2100.0830.2751.0000.0770.2080.1820.3560.2740.0800.3510.2151.0000.2080.1820.3560.2740.3510.2150.0830.0830.0370.0360.0000.0000.0850.0850.1810.0000.0000.0000.0450.0440.003
id_cliente_disp0.0860.1130.3150.0710.0771.0000.0680.0050.0310.0160.3330.0910.1020.0770.0680.0050.0310.0160.0910.1021.0000.3150.3770.0380.0110.0000.6470.0870.0361.0001.0001.0000.4210.4070.000
id_estatus_reservacion0.2020.1680.0350.1420.2080.0681.0000.0520.1800.1540.1750.1770.1880.2081.0000.0520.1800.1540.1770.1880.0390.0350.0300.0300.0000.0020.0420.0420.0620.0170.0170.0170.0390.0380.027
id_pais_origen0.1280.0240.0000.1220.1820.0050.0521.0000.0340.0020.0320.1220.0320.1820.0521.0000.0340.0020.1220.0320.0000.0000.0000.0000.0070.0000.0040.0040.0920.0000.0000.0000.0000.0000.000
id_paquete0.3330.0420.0190.2410.3560.0310.1800.0341.0001.0000.0450.2930.0650.3560.1800.0341.0001.0000.2930.0650.0190.0190.0180.0180.0000.0000.0210.0210.0880.0000.0000.0000.0280.0270.000
id_programa0.4180.0590.0050.1120.2740.0160.1540.0021.0001.0000.0570.1570.0660.2740.1540.0021.0000.9990.1570.0660.0040.0050.0000.0000.0000.0000.0100.0100.0100.0000.0000.0000.0490.0490.000
id_reservaciones0.1280.0590.8330.0820.0800.3330.1750.0320.0450.0571.0000.0800.0660.0800.1750.0320.0450.0570.0800.066-0.8290.833-0.1430.119-0.8120.799-0.8170.8290.0620.9930.9930.993-0.8640.862-0.767
id_segmento0.5520.2520.1040.4400.3510.0910.1770.1220.2930.1570.0801.0000.2120.3510.1770.1220.2930.1571.0000.2120.1060.1040.0320.0320.0310.0280.1040.1030.5010.0000.0000.0000.0500.0490.008
id_tipo_habitacion0.1611.0000.1170.1430.2150.1020.1880.0320.0650.0660.0660.2121.0000.2150.1880.0320.0650.0660.2121.0000.1180.1170.0370.0370.0550.0540.1150.1140.0810.0000.0000.0000.0550.0540.014
nombre_canal0.3320.2100.0830.2751.0000.0770.2080.1820.3560.2740.0800.3510.2151.0000.2080.1820.3560.2740.3510.2150.0830.0830.0370.0360.0000.0000.0850.0850.1810.0000.0000.0000.0450.0440.003
nombre_estatus_reservacion0.2020.1680.0350.1420.2080.0681.0000.0520.1800.1540.1750.1770.1880.2081.0000.0520.1800.1540.1770.1880.0390.0350.0300.0300.0000.0020.0420.0420.0620.0170.0170.0170.0390.0380.027
nombre_pais_origen0.1280.0240.0000.1220.1820.0050.0521.0000.0340.0020.0320.1220.0320.1820.0521.0000.0340.0020.1220.0320.0000.0000.0000.0000.0070.0000.0040.0040.0920.0000.0000.0000.0000.0000.000
nombre_paquete0.3330.0420.0190.2410.3560.0310.1800.0341.0001.0000.0450.2930.0650.3560.1800.0341.0001.0000.2930.0650.0190.0190.0180.0180.0000.0000.0210.0210.0880.0000.0000.0000.0280.0270.000
nombre_programa0.4180.0590.0050.1120.2740.0160.1540.0021.0000.9990.0570.1570.0660.2740.1540.0021.0001.0000.1570.0660.0040.0050.0000.0000.0000.0000.0100.0100.0100.0000.0000.0000.0490.0490.000
nombre_segmento0.5520.2520.1040.4400.3510.0910.1770.1220.2930.1570.0801.0000.2120.3510.1770.1220.2930.1571.0000.2120.1060.1040.0320.0320.0310.0280.1040.1030.5010.0000.0000.0000.0500.0490.008
nombre_tipo_habitacion0.1611.0000.1170.1430.2150.1020.1880.0320.0650.0660.0660.2121.0000.2150.1880.0320.0650.0660.2121.0000.1180.1170.0370.0370.0550.0540.1150.1140.0810.0000.0000.0000.0550.0540.014
numero_adultos0.0980.112-0.9210.0810.0831.0000.0390.0000.0190.004-0.8290.1060.1180.0830.0390.0000.0190.0040.1060.1181.000-0.9210.169-0.1460.898-0.8930.981-0.9120.0200.9460.9460.9460.957-0.9560.857
numero_adultos_anio_anterior0.0970.1111.0000.0800.0830.3150.0350.0000.0190.0050.8330.1040.1170.0830.0350.0000.0190.0050.1040.117-0.9211.000-0.1450.170-0.8920.901-0.9110.9820.0200.9460.9460.946-0.9570.958-0.849
numero_menores0.0230.032-0.1450.0170.0370.3770.0300.0000.0180.000-0.1430.0320.0370.0370.0300.0000.0180.0000.0320.0370.169-0.1451.000-0.0230.157-0.1410.258-0.1440.0000.0950.0950.0950.155-0.1500.130
numero_menores_anio_anterior0.0230.0320.1700.0170.0360.0380.0300.0000.0180.0000.1190.0320.0370.0360.0300.0000.0180.0000.0320.037-0.1460.170-0.0231.000-0.1410.158-0.1440.2570.0000.0960.0960.096-0.1520.156-0.135
numero_noches0.0000.000-0.8920.0000.0000.0110.0000.0070.0000.000-0.8120.0310.0550.0000.0000.0070.0000.0000.0310.0550.898-0.8920.157-0.1411.000-0.8640.889-0.8830.0000.0070.0070.0070.928-0.9250.849
numero_noches_anio_anterior0.0000.0000.9010.0000.0000.0000.0020.0000.0000.0000.7990.0280.0540.0000.0020.0000.0000.0000.0280.054-0.8930.901-0.1410.158-0.8641.000-0.8840.8920.0000.0060.0060.006-0.9280.930-0.823
numero_personas0.0910.107-0.9110.0710.0850.6470.0420.0040.0210.010-0.8170.1040.1150.0850.0420.0040.0210.0100.1040.1150.981-0.9110.258-0.1440.889-0.8841.000-0.9020.0150.2300.2300.2300.947-0.9460.844
numero_personas_anio_anterior0.0900.1070.9820.0710.0850.0870.0420.0040.0210.0100.8290.1030.1140.0850.0420.0040.0210.0100.1030.114-0.9120.982-0.1440.257-0.8830.892-0.9021.0000.0150.2320.2320.232-0.9470.949-0.841
pais_agencia1.0000.0580.0201.0000.1810.0360.0620.0920.0880.0100.0620.5010.0810.1810.0620.0920.0880.0100.5010.0810.0200.0200.0000.0000.0000.0000.0150.0151.0000.0000.0000.0000.0020.0020.000
reservacion0.0000.0000.9460.0000.0001.0000.0170.0000.0000.0000.9930.0000.0000.0000.0170.0000.0000.0000.0000.0000.9460.9460.0950.0960.0070.0060.2300.2320.0001.0001.0001.0000.9980.9980.010
reservacion_anio_anterior0.0000.0000.9460.0000.0001.0000.0170.0000.0000.0000.9930.0000.0000.0000.0170.0000.0000.0000.0000.0000.9460.9460.0950.0960.0070.0060.2300.2320.0001.0001.0001.0000.9980.9980.010
reservacion_pendiente0.0000.0000.9460.0000.0001.0000.0170.0000.0000.0000.9930.0000.0000.0000.0170.0000.0000.0000.0000.0000.9460.9460.0950.0960.0070.0060.2300.2320.0001.0001.0001.0000.9980.9980.010
total_habitaciones0.0390.046-0.9570.0330.0450.4210.0390.0000.0280.049-0.8640.0500.0550.0450.0390.0000.0280.0490.0500.0550.957-0.9570.155-0.1520.928-0.9280.947-0.9470.0020.9980.9980.9981.000-0.9930.884
total_habitaciones_anio_anterior0.0380.0460.9580.0320.0440.4070.0380.0000.0270.0490.8620.0490.0540.0440.0380.0000.0270.0490.0490.054-0.9560.958-0.1500.156-0.9250.930-0.9460.9490.0020.9980.9980.998-0.9931.000-0.881
total_tarifa0.0000.015-0.8490.0000.0030.0000.0270.0000.0000.000-0.7670.0080.0140.0030.0270.0000.0000.0000.0080.0140.857-0.8490.130-0.1350.849-0.8230.844-0.8410.0000.0100.0100.0100.884-0.8811.000

Missing values

2025-05-11T19:29:55.012162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-11T19:29:56.174899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-11T19:29:57.579873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

id_reservacionesfecha_hoyfecha_reservacionfecha_llegadafecha_salidanumero_personasnumero_personas_anio_anteriornumero_adultosnumero_adultos_anio_anteriornumero_menoresnumero_menores_anio_anteriornumero_nochesnumero_noches_anio_anteriortotal_habitacionestotal_habitaciones_anio_anteriorid_programanombre_programaid_paquetenombre_paqueteid_segmentonombre_segmentoid_agencianombre_agenciaciudad_agenciaentidad_federativa_agenciapais_agenciaid_empresanombre_empresatotal_habitaciones_empresaid_tipo_habitacionnombre_tipo_habitacioncupo_tipo_habitacionclasificacion_tipo_habitacionid_canalnombre_canalid_pais_origennombre_pais_origenreservacion_pendienteid_estatus_reservacionnombre_estatus_reservacionclave_estadonombre_estadototal_tarifaid_monedafecha_ultimo_cambioreservacionreservacion_anio_anteriorid_cliente_dispcliente_disp_anio_anterior
002019-08-162019-08-162019-07-112019-10-1120200030101Ninguno1Walk In14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173525Estd 2q Sn12mst2.0Mst10Multivacaciones 2157MexicoTrue9SalidaEmxMéxico2659.9812019-10-11TrueFalse20
112019-10-222019-10-222019-01-122019-05-1240400040101Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173525Estd 2q Sn12mst2.0Mst10Multivacaciones 2157MexicoTrue9SalidaEgtGuanajuato1764.0012019-05-12TrueFalse40
222019-10-282019-10-282019-01-122019-05-1220200040101Ninguno1Walk In14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173523Jr Suite 1k Sn12mjs2.0Mjs10Multivacaciones 2157MexicoTrue9SalidaEmcMichoacán2660.0412019-05-12TrueFalse20
332019-10-282019-10-282019-08-122019-11-1240300030101Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173524Jr Suite 2q Sn12mjs2.0Mjs10Multivacaciones 2157MexicoTrue9SalidaEgtGuanajuato1995.0312019-11-12TrueFalse30
442019-10-282019-10-282019-08-122019-12-1220200040101Ninguno1Walk In5Ecommerce Ota Domestic14Bestday Travel GroupCancúnQuintana RooMéxico1Hotel 17354Luxury 2q Sn12gsu2.0Gsu13Vertical Booking157MexicoTrue9SalidaEmxMéxico13369.9212019-12-12TrueFalse20
552019-05-082019-05-082019-01-122019-05-1260600040101Ninguno1Walk In14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173524Jr Suite 2q Sn12mjs2.0Mjs12Sitio Propio157MexicoTrue9SalidaEgtGuanajuato2660.0412019-05-12TrueFalse60
662019-07-192019-07-192019-08-222019-08-2520200030101Ninguno1Walk In14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173525Estd 2q Sn12mst2.0Mst10Multivacaciones 2157MexicoTrue2Reservacion CanceladaEgrGuerrero3331.9812019-07-24TrueFalse20
772019-07-192019-07-192019-08-232019-08-2520200020101Ninguno1Walk In5Ecommerce Ota Domestic14Bestday Travel GroupCancúnQuintana RooMéxico1Hotel 17354Luxury 2q Sn12gsu2.0Gsu1Lada 800 Nacional68157MexicoTrue9SalidaEmcMichoacán8809.9212019-08-25TrueFalse20
882019-07-192019-07-192019-08-252019-08-2930200040101Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173526Mv Luxury 2q Sn12gsu2.0Gsu10Multivacaciones 2157MexicoTrue9SalidaEgrGuerrero2548.0012019-08-29TrueFalse20
992019-06-052019-06-052019-08-252019-08-2920200040101Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173527Mv Luxury 1k Sn12gsu2.0Gsu10Multivacaciones 2157MexicoTrue9SalidaEmcMichoacán2548.0012019-08-29TrueFalse20
id_reservacionesfecha_hoyfecha_reservacionfecha_llegadafecha_salidanumero_personasnumero_personas_anio_anteriornumero_adultosnumero_adultos_anio_anteriornumero_menoresnumero_menores_anio_anteriornumero_nochesnumero_noches_anio_anteriortotal_habitacionestotal_habitaciones_anio_anteriorid_programanombre_programaid_paquetenombre_paqueteid_segmentonombre_segmentoid_agencianombre_agenciaciudad_agenciaentidad_federativa_agenciapais_agenciaid_empresanombre_empresatotal_habitaciones_empresaid_tipo_habitacionnombre_tipo_habitacioncupo_tipo_habitacionclasificacion_tipo_habitacionid_canalnombre_canalid_pais_origennombre_pais_origenreservacion_pendienteid_estatus_reservacionnombre_estatus_reservacionclave_estadonombre_estadototal_tarifaid_monedafecha_ultimo_cambioreservacionreservacion_anio_anteriorid_cliente_dispcliente_disp_anio_anterior
2029922029922020-05-022019-05-022019-04-192019-04-2604020207011Ninguno1Walk In18Tour Operators Internationals116Sunwing VacationsTorontoOntarioCanada1Hotel 173517Estd C/balcon Sn12asb2.0Asb0Sin Definir38CanadaFalse9SalidaEcaCalifornia0.012019-04-26FalseTrue02
2029932029932020-10-042019-10-042019-10-042019-04-1302020003011Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173519Estd Db Sn12ast2.0Ast1Lada 800 Nacional680Sin DefinirFalse9SalidaEmcMichoacán0.012019-04-13FalseTrue02
2029942029942020-10-042019-10-042019-10-042019-04-1302020003011Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173519Estd Db Sn12ast2.0Ast1Lada 800 Nacional680Sin DefinirFalse9SalidaEmcMichoacán0.012019-04-13FalseTrue02
2029952029952020-10-042019-10-042019-10-042019-04-1302020003011Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173519Estd Db Sn12ast2.0Ast1Lada 800 Nacional680Sin DefinirFalse9SalidaEmcMichoacán0.012019-04-13FalseTrue02
2029962029962020-10-042019-10-042019-10-042019-10-0402020003011Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173519Estd Db Sn12ast2.0Ast1Lada 800 Nacional680Sin DefinirFalse2Reservacion CanceladaEmcMichoacán0.012019-10-04FalseTrue02
2029972029972020-10-042019-10-042019-10-042019-10-0402020003011Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173519Estd Db Sn12ast2.0Ast1Lada 800 Nacional680Sin DefinirFalse2Reservacion CanceladaEmcMichoacán0.012019-10-04FalseTrue02
2029982029982020-06-172019-06-172019-06-172019-06-1702020000011Ninguno1Walk In14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173513Sup Luj King Sn12asb2.0Asb1Lada 800 Nacional680Sin DefinirFalse9SalidaEgrGuerrero0.012019-06-17FalseTrue02
2029992029992020-05-302019-05-302019-05-302019-02-0603020003011Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173517Estd C/balcon Sn12asb2.0Asb1Lada 800 Nacional680Sin DefinirFalse9SalidaEmcMichoacán0.012019-02-06FalseTrue02
2030002030002020-10-082019-10-082019-10-082019-12-0802020002011Ninguno1Walk In5Ecommerce Ota Domestic14Bestday Travel GroupCancúnQuintana RooMéxico1Hotel 17356Luxury 1k Sn12gsu2.0Gsu1Lada 800 Nacional680Sin DefinirFalse9SalidaEmcMichoacán0.012019-12-08FalseTrue02
2030012030012020-02-242019-02-242019-02-242019-02-2802020004011Ninguno2Ninguno14Individual Ep/vac. Club112Hoteles S.a.Mexico CityMéxicoMéxico1Hotel 173525Estd 2q Sn12mst2.0Mst1Lada 800 Nacional680Sin DefinirFalse9SalidaEmcMichoacán0.012019-02-28FalseTrue02